Global Generative Artificial Intelligence (AI) in Personalized Medicine Market to Reach US$1.5 Billion by 2030

The global market for Generative Artificial Intelligence (AI) in Personalized Medicine estimated at US$244.9 Million in the year 2024, is expected to reach US$1.5 Billion by 2030, growing at a CAGR of 35.9% over the analysis period 2024-2030. Cloud-based Deployment, one of the segments analyzed in the report, is expected to record a 39.4% CAGR and reach US$1.0 Billion by the end of the analysis period. Growth in the On-Premise Deployment segment is estimated at 30.2% CAGR over the analysis period.

The U.S. Market is Estimated at US$77.8 Million While China is Forecast to Grow at 42.7% CAGR

The Generative Artificial Intelligence (AI) in Personalized Medicine market in the U.S. is estimated at US$77.8 Million in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$228.5 Million by the year 2030 trailing a CAGR of 42.7% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 32.5% and 29.8% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 36.2% CAGR.

Global "Generative Artificial Intelligence (AI) in Personalized Medicine" Market – Key Trends & Drivers Summarized

Why Is the Intersection of Generative AI and Personalized Medicine Gaining Momentum?
The integration of generative artificial intelligence (AI) into personalized medicine represents a paradigm shift in healthcare, one that is being rapidly propelled by the convergence of computational innovation, genomic science, and patient-centered care models. Generative AI, which includes models capable of creating novel data outputs—such as synthetic biomarker profiles, treatment simulations, or drug molecule structures—has found a critical role in tailoring medical interventions to the individual level. This trend is being catalyzed by the exponential growth of multi-dimensional healthcare data, including genomic sequencing, electronic health records (EHRs), imaging files, and patient lifestyle data. As a result, AI systems can now generate predictive models that simulate disease progression or treatment outcomes with unprecedented accuracy. Hospitals and research institutions are leveraging these capabilities to refine diagnosis, personalize care plans, and streamline clinical workflows. The digital infrastructure underpinning healthcare delivery is also evolving to accommodate AI applications, supported by increasing interoperability between platforms and regulatory frameworks that promote innovation while ensuring patient safety. Additionally, data-rich regions such as North America and parts of Asia-Pacific are witnessing a surge in AI adoption, driven by their robust biotechnology ecosystems and supportive funding landscapes. Together, these elements are reshaping the healthcare value chain and positioning generative AI as a cornerstone technology in the evolution of personalized medicine.

How Is Drug Discovery Being Transformed by Generative AI?
One of the most disruptive impacts of generative AI in personalized medicine is observed in the drug discovery and development sector, where AI is fundamentally altering how pharmaceutical products are designed, tested, and approved. Traditional drug discovery is resource-intensive and time-consuming, often requiring over a decade and billions in investment to bring a new drug to market. Generative models—especially those built on architectures like variational autoencoders (VAEs), transformers, and diffusion models—can rapidly generate and evaluate thousands of potential compounds in silico, dramatically compressing the early stages of R&D. These models are particularly effective in identifying drug candidates that align with a patient`s unique genetic mutations or biomarkers, thereby aligning with the core principles of personalized medicine. Further, AI can simulate how different molecules interact with human proteins or predict the likelihood of side effects based on population-specific data, improving drug safety profiles. Startups and pharma giants alike are adopting AI-first pipelines to target niche conditions, such as rare genetic diseases, where conventional drug models are not economically viable. Partnerships between tech companies and pharmaceutical firms have become central to accelerating this trend, often resulting in joint ventures or dedicated AI drug discovery platforms. Intellectual property is also evolving, as AI-generated compounds challenge existing frameworks for patentability. Regulatory bodies are responding with adaptive guidelines, allowing faster validation of AI-developed therapies. Altogether, this technological shift is making the development of personalized therapeutics more agile, cost-efficient, and precise than ever before.

What Role Do Patients and Healthcare Providers Play in Shaping This Market?
The broader adoption of generative AI in personalized medicine is not only a function of technological readiness but also reflects changing behaviors among patients, providers, and healthcare systems. Modern patients, increasingly empowered by access to their own health data through wearables and mobile apps, are driving demand for customized care solutions. They are more inclined to seek out precision treatments that reflect their unique genetic and lifestyle profiles, creating a fertile market for AI-based diagnostic and therapeutic tools. At the same time, healthcare providers are integrating AI-powered decision support systems that generate individualized treatment recommendations, simulate treatment outcomes, and even produce personalized clinical documentation. This enhances clinical productivity and improves patient outcomes, as physicians can make more informed, data-backed decisions. From a systems perspective, hospitals and clinics are transitioning to AI-integrated electronic health record systems that enable real-time, multi-source data fusion—crucial for the success of personalized medicine. Insurers are also beginning to adapt, using AI to underwrite personalized health plans and predictive risk models. Meanwhile, academic institutions are incorporating AI modules into medical training programs, preparing future healthcare professionals to operate in this AI-augmented environment. The collective shift toward personalized, AI-enabled care delivery is further reinforced by public health initiatives that promote preventive care and chronic disease management through individualized risk assessments. These ecosystem-wide changes indicate a growing alignment between consumer expectations and technological capabilities, solidifying generative AI’s role in the personalization of medicine.

What’s Fueling the Surge in Global Market Demand for Generative AI in Personalized Medicine?
The growth in the generative AI in personalized medicine market is driven by several factors directly tied to technological evolution, end-user expansion, and systemic transformation. First, the availability of large-scale, high-resolution datasets—from genomics, transcriptomics, proteomics, and patient histories—creates the ideal training environment for generative models, enhancing their ability to produce clinically relevant insights. Second, the decreasing cost of whole-genome sequencing and molecular diagnostics has significantly expanded the user base, making personalized care accessible to a broader population. On the technological front, rapid advancements in cloud infrastructure, edge computing, and large language model (LLM) architectures have made it feasible to process vast biomedical datasets in real time. End-use sectors such as oncology, neurology, and immunology are increasingly integrating generative AI to target disease subtypes with personalized treatment pathways, leading to better clinical efficacy and reduced adverse effects. Additionally, pharma and biotech companies are investing heavily in AI collaborations to accelerate pipeline development and reduce R&D overhead. Clinical trial optimization is another critical driver, with AI-generated virtual cohorts being used to simulate outcomes and reduce dependency on large-scale patient recruitment. Government grants, venture capital flows, and public-private initiatives are reinforcing this trend, especially in innovation hubs across the U.S., China, Germany, and South Korea. Regulatory environments are also evolving to accommodate AI-based submissions, offering faster turnaround times and greater flexibility in trial designs. Lastly, growing patient preference for individualized care plans, coupled with clinician trust in AI-generated outputs, is fueling adoption across healthcare ecosystems. Collectively, these drivers point to a robust and expanding global market for generative AI applications in personalized medicine.

SCOPE OF STUDY:

The report analyzes the Generative Artificial Intelligence (AI) in Personalized Medicine market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Deployment (On-Premise Deployment, Cloud Deployment); Type (Personalized Pharmaceuticals, Personalized Genomic Medicines, Personalized Devices); Application (Hospitals & Clinics Application, Ambulatory Surgery Centers Application, Other Applications)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 63 Featured) -

• 1910 Genetics
• Accenture
• Ada Health GmbH
• Aidoc
• Aindo Srl
• Aisera
• Aitia (formerly GNS Healthcare)
• AKin AI
• Amazon Web Services, Inc.
• Antiverse

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